For communication systems utilizing periodically transmitted training sequences, least-squares (LS) based and correlation based channel-estimation algorithms have been the most widely used two alternatives for the estimation of the channel. The estimation of the channel is obtained for various purposes such as initializing the tap weights of the taps of an equalizer in a receiver.
Both algorithms (the least-squares (LS) based channel estimation and the correlation based channel estimation) use a stored copy of the known transmitted training sequence at the receiver. The properties and length of the training sequence generally depend on the particular standard specifications of the communication system. A baseline noise term is introduced into the channel estimation whenever the channel impulse response is estimated by a correlation type estimator or by a least square type estimator. This noise term will be increased if the training sequence is short compared to the length of the channel. This noise term results, at least in part, from the correlation of the stored copy of the training sequence with unknown symbols in addition to the known received training symbols. These unknown symbols includes the data symbols that are adjacent to transmitted training sequence. This baseline noise term reduces the accuracy of the channel estimations.
The present invention is directed to the estimation of a channel impulse response (CIR) based both on the training sequence that is periodically transmitted within a continuous stream of information symbols and on the statistics of the unknown data adjacent to the training sequence. Accordingly, this estimation may be referred to as the best linear unbiased estimate of the channel, meaning that the estimate has reduced or minimum variance, that the estimate is based on a linear model, and that the expected value of the estimate is substantially equal to the true value. Accordingly, the noise term in the channel estimation is reduced or eliminated according to the present invention.